2021-05-17 21:24:25 +02:00
|
|
|
function [h_minus_1, h, h_plus_1, h_exo, resid] = get_deriv(M_, ys_)
|
|
|
|
% function [h_minus_1, h, h_plus_1, h_exo, resid] = get_deriv(M_, ys_)
|
|
|
|
% Computes dynamic Jacobian and writes it to conformable matrices
|
|
|
|
%
|
|
|
|
% INPUTS
|
|
|
|
% - M_ [struct] Definition of the model.
|
|
|
|
% - ys vector steady state
|
|
|
|
%
|
|
|
|
% OUTPUTS
|
|
|
|
% - h_minus_1 [N by N] derivative matrix with respect to lagged endogenous variables
|
|
|
|
% - h [N by N] derivative matrix with respect to contemporanous endogenous variables
|
|
|
|
% - h_plus_1 [N by N] derivative matrix with respect to leaded endogenous variables
|
|
|
|
% - h_exo [N by N_exo] derivative matrix with respect to exogenous variables
|
|
|
|
% - resid [N by 1] vector of residuals
|
|
|
|
|
2022-04-13 13:15:19 +02:00
|
|
|
% Copyright © 2021 Dynare Team
|
2021-05-17 21:24:25 +02:00
|
|
|
%
|
|
|
|
% This file is part of Dynare.
|
|
|
|
%
|
|
|
|
% Dynare is free software: you can redistribute it and/or modify
|
|
|
|
% it under the terms of the GNU General Public License as published by
|
|
|
|
% the Free Software Foundation, either version 3 of the License, or
|
|
|
|
% (at your option) any later version.
|
|
|
|
%
|
|
|
|
% Dynare is distributed in the hope that it will be useful,
|
|
|
|
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
|
|
|
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
|
|
|
% GNU General Public License for more details.
|
|
|
|
%
|
|
|
|
% You should have received a copy of the GNU General Public License
|
|
|
|
% along with Dynare. If not, see <https://www.gnu.org/licenses/>.
|
|
|
|
|
|
|
|
x = zeros(M_.maximum_lag + M_.maximum_lead + 1,M_.exo_nbr);
|
|
|
|
|
|
|
|
iyv = M_.lead_lag_incidence';
|
|
|
|
iyr0 = find(iyv(:)) ;
|
|
|
|
z=repmat(ys_,1,M_.maximum_lag + M_.maximum_lead + 1);
|
|
|
|
|
|
|
|
[resid,g1]=feval([M_.fname,'.dynamic'],z(iyr0),x, M_.params, ys_, M_.maximum_exo_lag+1);
|
|
|
|
|
|
|
|
% Initialize matrices
|
|
|
|
h_minus_1=zeros(M_.endo_nbr);
|
|
|
|
h = h_minus_1;
|
|
|
|
h_plus_1 = h_minus_1;
|
|
|
|
|
|
|
|
% build h_minus_1
|
|
|
|
if M_.maximum_lag
|
|
|
|
lag_columns=find(iyv(:,1));
|
|
|
|
n_lag_columns=length(lag_columns);
|
|
|
|
h_minus_1(:,lag_columns) = g1(:,1:n_lag_columns);
|
|
|
|
else
|
|
|
|
n_lag_columns=0;
|
|
|
|
end
|
|
|
|
|
|
|
|
% build h
|
|
|
|
contemporaneous_columns=find(iyv(:,M_.maximum_lag+1));
|
|
|
|
n_contemporaneous_columns = length(contemporaneous_columns);
|
|
|
|
h(:,contemporaneous_columns) = g1(:,1+n_lag_columns:n_lag_columns+n_contemporaneous_columns);
|
|
|
|
|
|
|
|
%build h_plus_1
|
|
|
|
if M_.maximum_lead
|
|
|
|
lead_columns=find(iyv(:,end));
|
|
|
|
n_lead_columns = length(lead_columns);
|
|
|
|
h_plus_1(:,lead_columns) = g1(:,n_lag_columns+n_contemporaneous_columns+1:n_lag_columns+n_contemporaneous_columns+n_lead_columns);
|
|
|
|
else
|
|
|
|
n_lead_columns=0;
|
|
|
|
end
|
|
|
|
|
|
|
|
h_exo =g1(:,n_lag_columns+n_contemporaneous_columns+n_lead_columns+1:end);
|